Estimating the Heading Direction Using Normal Flow

Abstract

If an observer is moving rigidly with bounded rotation then normal flow measurements (i.e., the spatiotemporal derivatives of the image intensity function) give rise to a constraint on the observer's translation. This novel constraint gives rise to a robust, qualitative solution to the problem of recovering the observer's heading direction, by providing an area where the Focus of Expansion lies. If the rotation of the observer is large then the solution area is large too, while small rotation causes the solution area to be small, thus giving rise to a robust solution. In the paper the relationship between the solution area and the rotation and translation vectors is studied and experimental results using synthetic and real calibrated image sequences are presented. This work demonstrates that the algorithm developed for the case of pure translation, if appropriately modified, results in a robust algorithm that works in the case of general rigid motion with bounded rotation. Subsequently, it has the potential to replace expensive accelerometers, inertial systems and inaccurate odometers in practical navigational systems for the problem of kinetic stabilization, which is a prerequisite for any other navigational ability.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1994
Accession Number
ADA285492

Entities

People

  • Yiannis Aloimonos
  • Zoran Duric

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Boundaries
  • Computations
  • Computer Science
  • Computer Vision
  • Computers
  • Coordinate Systems
  • Detectors
  • Flow Fields
  • Geometry
  • Image Processing
  • Inertial Navigation Systems
  • Measurement
  • Navigation
  • Three Dimensional
  • Universities

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Inertial Navigation Systems.